Image filtering (gaussian_filter) in SciPy - Mini Project: Build & Apply

Image filtering with gaussian_filter

📖 Scenario: You have a small grayscale image represented as a 2D list of pixel brightness values. You want to smooth the image to reduce noise and make it look softer, like when you blur a photo slightly.

🎯 Goal: Apply a Gaussian filter to the image data using scipy.ndimage.gaussian_filter to create a smooth version of the image.

📋 What You'll Learn

Create a 2D list called image with exact pixel values

Create a variable called sigma to set the blur strength

Use scipy.ndimage.gaussian_filter with image and sigma

Print the filtered image array

💡 Why This Matters

🌍 Real World

Image smoothing is used in photography and computer vision to reduce noise and improve image quality.

💼 Career

Understanding image filtering is important for roles in data science, machine learning, and computer vision engineering.

1

Create the image data

Create a 2D list called image with these exact pixel values: [[10, 20, 30], [20, 30, 40], [30, 40, 50]]

SciPy

# Create the 2D list called image with the given pixel values
# Your code here

Need a hint?

Think of image as a small grid of brightness numbers.

2

Set the blur strength

Create a variable called sigma and set it to 1 to control the amount of blur.

SciPy

image = [[10, 20, 30], [20, 30, 40], [30, 40, 50]]
# Set sigma to 1
# Your code here

Need a hint?

The sigma value controls how much the image will be blurred.

3

Apply the Gaussian filter

Import gaussian_filter from scipy.ndimage and create a variable called filtered_image by applying gaussian_filter to image with sigma.

SciPy

image = [[10, 20, 30], [20, 30, 40], [30, 40, 50]]
sigma = 1
# Import gaussian_filter and apply it to image with sigma
# Your code here

Need a hint?

Use gaussian_filter(image, sigma) to smooth the image.

4

Print the filtered image

Print the variable filtered_image to see the smoothed image values.

SciPy

from scipy.ndimage import gaussian_filter

image = [[10, 20, 30], [20, 30, 40], [30, 40, 50]]
sigma = 1

filtered_image = gaussian_filter(image, sigma)
# Print the filtered_image
# Your code here

Need a hint?

The printed output shows the smoothed pixel values as a NumPy array.